Data Science and AI Intern - RTP 2027

About the role

We are seeking a motivated Intern to join our AI and Data Science development team. In this hands-on technical role, you will contribute to the design, development, and implementation of AI solutions, while also supporting the integration and performance of these systems. You’ll work on efficiently collecting, cleaning, analyzing, and visualizing data to support business decisions and real-world applications across enterprise systems.

You’ll collaborate closely with engineering, support, and senior developers to ensure AI models are robust, scalable, and aligned with business needs. Responsibilities include helping create predictive models, generating insights, and optimizing company performance. This role is ideal for someone passionate about AI and eager to grow their skills in both development and systems-level implementation.

Responsibilities

• Contribute to the design, development, and implementation of AI solutions.
• Support the integration and performance of AI systems.
• Collect, clean, analyze, and visualize data to support business decisions.
• Work with engineering and senior developers to ensure AI models are robust and scalable.
• Help create predictive models and generate insights to optimize performance.

Requirements

Required Technical and Professional Expertise

• Solid understanding of Python and/or another programming language commonly used in AI (e.g., Java, C++, or R).
• Foundational knowledge in linear algebra, calculus, probability, and statistics.
• Familiarity with core machine learning concepts such as supervised and unsupervised learning, model evaluation, and overfitting.
• Experience working with data using libraries like NumPy, pandas, or similar.
• Basic understanding of Git and collaborative development workflows.
• Ability to clearly articulate technical concepts and collaborate effectively in a team environment.

Preferred Technical and Professional Experience

• Exposure to deep learning frameworks like TensorFlow, PyTorch, or JAX.
• Prior coursework, personal projects, or research involving AI/ML.
• Familiarity with cloud services like AWS, Azure, or Google Cloud for AI workloads.
• Understanding of software engineering practices, including testing, debugging, and code optimization.
• Experience with data visualization tools like Matplotlib, Seaborn, or Plotly.
• Awareness of AI ethics, including fairness, bias, and responsible AI development practices.